CN113391988A

CN113391988A - Method and device for losing user retention, electronic equipment and storage medium

Info

Publication number: CN113391988A
Application number: CN202110730889.9A
Authority: CN
Inventors: 鲁转丽; 周洪菊; 李倩; 郭志军
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2021-09-14

Abstract

The present disclosure provides a method for losing user retention, which can be applied to the financial field, the big data field or other fields. The method comprises the following steps: acquiring user information of a user, wherein the user information comprises user basic attribute information and user portrait information, and the user portrait information is used for representing the access condition of the user within a preset time period; inputting user information of a user into a neural network model to obtain a first probability which is output by the neural network model and used for representing the user loss degree; screening operation is carried out on the users based on the first probability, and a potential lost user list is generated; and pushing targeted financial product information to the users in the potential lost user list according to the basic attribute information of the users. The present disclosure also provides an apparatus, device, storage medium and program product for attrition of user retention.

Description

Method and device for losing user retention, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of big data technologies, and more particularly, to a method, an apparatus, an electronic device, a storage medium, and a computer program product for losing user retention.

Background

With the development of the internet, competition among enterprises is more and more intense, and user loss is more and more serious. Therefore, how to accurately predict and intelligently save the lost users by means of technical analysis is very important.

Disclosure of Invention

In view of the above, the present disclosure provides a method, an apparatus, an electronic device, a storage medium, and a computer program product for churning user retention.

According to one aspect of the present disclosure, there is provided a method of churning user retention, comprising:

acquiring user information of a user, wherein the user information comprises user basic attribute information and user portrait information, and the user portrait information is used for representing the access condition of the user within a preset time period;

inputting user information of the user into a neural network model, and obtaining a first probability which is output by the neural network model and used for representing the user loss degree;

performing screening operation on the users based on the first probability, and generating a potential attrition user list;

and pushing targeted financial product information to the users in the potential lost user list according to the user basic attribute information.

According to an embodiment of the present disclosure, the method further comprises:

classifying the users according to the user information of the users by using an unsupervised clustering algorithm to obtain a potential lost user group;

and inputting the user information of the users in the potential user loss group into the neural network model, and obtaining a first probability which is output by the neural network model and used for representing the user loss degree.

According to an embodiment of the present disclosure, the performing a filtering operation on the user based on the first probability to generate a list of potential attrition users includes:

and comparing the first probability with a first preset threshold, and adding the user to the potential attrition user list under the condition that the first probability is not less than the first preset threshold.

According to an embodiment of the present disclosure, the method further comprises: periodically sending targeted financial product information to at least one user in the list of potentially attrition users.

According to an embodiment of the present disclosure, the method further comprises: and comparing the first probability with a second preset threshold, and adding the user to a lost user list under the condition that the first probability is not less than the second preset threshold.

According to an embodiment of the present disclosure, wherein the user portrait information includes at least one of:

the number of times that the user logs in the mobile client;

the number of times the user makes transactions;

the duration of time that the user accesses the mobile client; and

the user is asked a number of times of content related to the financial product, wherein the content related to the financial product includes at least one of a product introduction of the financial product, offer information of the financial product, and other products associated with the financial product.

According to an embodiment of the present disclosure, the user basic attribute information includes at least one of user age, user gender, educational background, professional background, asset information.

According to an embodiment of the present disclosure, the financial product information includes at least one of offer information, financial product introduction, and status information of a financial product.

According to another aspect of the present disclosure, there is provided an apparatus to attrition user retention, comprising:

the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring user information of a user, and the user information comprises user basic attribute information and user portrait information, and the user portrait information is used for representing the access condition of the user within a preset time period;

the screening module is used for inputting the user information of the user into a neural network model and obtaining a first probability which is output by the neural network model and used for representing the user loss degree;

a first adding module, configured to perform a screening operation on the user based on the first probability, and generate a list of potential attrition users;

and the pushing module is used for pushing targeted financial product information to the users in the potential lost user list according to the user basic attribute information.

According to an embodiment of the present disclosure, the apparatus further comprises:

the classification module is used for classifying the users according to the user information of the users by using an unsupervised clustering algorithm to obtain a potential lost user group;

the screening module is further used for inputting user information of users in the potential churning user group into the neural network model, and obtaining a first probability which is output by the neural network model and used for representing the churning degree of the users.

According to an embodiment of the present disclosure, the first adding module is further configured to:

an update module to periodically send targeted financial product information to at least one user in the list of potentially attrition users.

and the second adding module is used for comparing the first probability with a second preset threshold value, and adding the user into a lost user list under the condition that the first probability is not less than the second preset threshold value.

the number of times that the user logs in the mobile client;

the number of times the user makes transactions;

the duration of time that the user accesses the mobile client; and

a number of times a user accesses content related to the financial product, wherein the content related to the financial product includes at least one of a product description of the financial product, offer information for the financial product, and other products associated with the financial product.

According to another aspect of the present disclosure, there is provided an electronic device including: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform operations that implement the methods described above.

According to another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform a method implementing the above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario of a method, apparatus, device, storage medium and computer program product of attrition user retention according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a method of churning user retention according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure;

FIG. 4 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure;

FIG. 6 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure;

FIG. 7 schematically illustrates a block diagram of an apparatus that churns user retention according to an embodiment of the present disclosure;

FIG. 8 schematically illustrates a block diagram of an apparatus that churns user retention according to another embodiment of the present disclosure;

FIG. 9 schematically illustrates a block diagram of an apparatus that churns user retention according to another embodiment of the present disclosure;

FIG. 10 schematically illustrates a block diagram of an apparatus that churns user retention according to another embodiment of the present disclosure; and

fig. 11 schematically illustrates a block diagram of an electronic device suitable for implementing a method of churning user retention according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.

Embodiments of the present disclosure provide a method, an apparatus, an electronic device, a storage medium, and a computer program product for churning user retention, which may be used in the financial field, the big data field, or other fields, and are not limited herein. The method comprises the following steps: acquiring user information of a user, wherein the user information comprises user basic attribute information and user portrait information, and the user portrait information is used for representing the access condition of the user within a preset time period; inputting user information of a user into a neural network model to obtain a first probability which is output by the neural network model and used for representing the user loss degree; screening operation is carried out on the users based on the first probability, and a potential lost user list is generated; and pushing targeted financial product information to the users in the potential lost user list according to the basic attribute information of the users. By adopting the method, the potential loss user can be accurately predicted in a simple and efficient manner, and can be intelligently stored.

Fig. 1 schematically illustrates an application scenario diagram of a method, apparatus, device, storage medium and computer program product for churning user retention according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the application scenario 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the method for churning user retention provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the mechanism for churning user retention provided by embodiments of the present disclosure may be generally located in the server 105. The method of churning user retention provided by embodiments of the present disclosure may also be performed by a server or server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the apparatus for churning user retention provided by the embodiments of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The method for churning user retention according to the embodiment of the present disclosure will be described in detail below with reference to fig. 2 to 6 based on the scenario described in fig. 1.

FIG. 2 schematically shows a flow diagram of a method of churning user retention according to an embodiment of the present disclosure.

As shown in FIG. 2, the method for losing user retention specifically includes operations S210-S240.

In operation S210, user information of a user is obtained, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

The user basic attribute information referred to herein includes, but is not limited to, user age, user gender, educational background, professional background, asset information, and the like. User profile information includes, but is not limited to, the number of times a user logs into a mobile client, the number of times a user makes a transaction, the length of time a user accesses a mobile client, and the number of times a user accesses content related to a financial product. The content related to the financial product may be, for example, a product description of the financial product, offer information (e.g., a coupon, etc.) of the financial product, other products related to the financial product, and the like, which is not limited herein.

In operation S210, the user information of the user may be obtained, for example, the user information of one user, or the user information of a plurality of users, which is not limited herein.

In operation S220, user information of a user is input into the neural network model, and a first probability output by the neural network model and used for representing a user churn degree is obtained.

The neural network model referred to herein is a neural network model that has been trained in advance using user information. The user information comprises user basic attribute information and user portrait information, and the user portrait information is used for representing the access condition of a user in a preset time period. The training process of the neural network model is simply described as follows:

the method comprises the steps of firstly, obtaining user information of a plurality of users, wherein the user information comprises user basic attribute information and user portrait information. And processing the user information of a plurality of users to obtain training data.

Specifically, the data is labeled by using expert rules, for example, "0" indicates loss, and "1" indicates non-loss (hereinafter referred to as label information). In addition, fields in the user information are converted into field values, that is, specific values are used to indicate specific meanings of the fields in the user information, for example, in the user basic attribute information, the user age is indicated by "30" (that is, the user age is indicated by 30 years), and the user asset information is indicated by "5" (that is, the user asset is indicated by 5 ten thousand yuan). The conversion method of the user portrait information is similar to the conversion method of the user basic attribute information, for example, if the number of times the user logs in the mobile client is 3, the number of times the user logs in the mobile client is represented by "3". And then, sequentially connecting the label information, the user basic attribute information and the user portrait information according to the sequence of the label information, the user basic attribute information and the user portrait information to form training data. Each piece of training data may be understood as a feature, and the neural network is trained based on the feature.

And step two, screening the data characteristics and deleting redundant characteristics.

Specifically, redundant features may be deleted according to actual needs. For example, a random forest algorithm can be used for screening the data features, redundant or unnecessary features are deleted, training data more suitable for practical application are obtained, and prediction accuracy of the neural network model is improved.

And step three, training the neural network model according to the training data to obtain the trained neural network model.

In this step, the screened training data is input into a neural network for neural network model training, and the training mode is the same as or similar to that of the prior art, and is not repeated here.

In operation S220, user information of the user is input into the trained neural network model, and a first probability output by the neural network model and used for representing the user churn degree is obtained.

For example, inputting user information of one or more users into the trained neural network model may correspond to one or more first probabilities representing the degree of user churn. In this embodiment, the larger the value of the first probability is, the higher the possibility that the user runs away is, and it is necessary to pay attention. Conversely, the smaller the value of the first probability, the less the possibility that the user is lost, and the attention of the user may be low or may not be paid.

In operation S230, a screening operation is performed on the users based on the first probability, and a list of potential attrition users is generated.

In the embodiment of the disclosure, users can be screened based on the first probability, and users meeting preset conditions are added to the list of potential lost users, by adopting the above manner, the potential lost users can be obtained, and then targeted retention can be performed on the users in the list of the potential lost users, so that the retention effect of the potential lost users can be improved.

In some embodiments of the present disclosure, the users may be classified according to the magnitude of the probability value, for example, the probability value output by the neural network may be divided into three levels, i.e., a high probability, a medium probability and a low probability, where the high probability indicates that the users are away most likely, the medium probability indicates that the users are away more likely, and the low probability indicates that the users are away least likely. The high probability may be, for example, a probability value of 60% or more, the medium probability may be, for example, a probability value of 60% to 30%, and the low probability may be, for example, a probability value of 30% or less. Those skilled in the art will appreciate that the expressions "high", "medium", "low" and specific numerical values are only examples provided for the convenience of understanding the present solution, and the present application does not limit the specific numerical values of the probabilities and the division of the levels.

The preset condition may be, for example, that the first probability satisfies three levels, i.e., a high probability, a medium probability, and a low probability, so that the potential churning users may be divided into a plurality of different levels, and then different attention degrees may be given to the potential churning users of different levels, for example, when the first probability satisfies the high probability, the probability that the user churns is the highest, and special attention needs to be given; when the first probability meets the medium probability, the probability of user loss is medium, and important attention needs to be given; when the first probability satisfies the low probability, the probability of indicating the user loss is small, and low attention or no attention may be given. The processing mode is more targeted, and the effect of the retention of the potentially lost user can be further improved.

In operation S240, targeted financial product information is pushed to users in the list of potential attrition users according to the user basic attribute information.

In operation S240, financial product information that may be of interest is pushed to the users in the list of users with potential churn according to the basic attribute information of the users (e.g., user age, user gender, user education background, professional background, etc.), so as to achieve the purpose of remaining users with potential churn.

The financial product information herein includes, but is not limited to, offer information of financial products, introduction of financial products, and status information of financial products. The status information of the financial product may indicate coupon information of the financial product, whether the financial product introduction is updated, for example, if the coupon information (e.g., discount coupon, full discount coupon, cash back coupon, cash voucher, etc.) of the financial product, the financial product introduction has been changed within a previous preset time period, the status information indicates that the financial product information is the updated financial product information. The status information may also indicate whether the financial product information is valid. For example, where the financial product information is a coupon, the status information may indicate whether the coupon is valid.

It should be noted that the above description of pushing financial product information is only an example for facilitating understanding of the present solution, and the present application does not limit the content pushed to the potentially-churned user.

According to the loss user retention method, the user basic attribute information and the user portrait information are input into the neural network to predict user loss, so that the potential loss user can be accurately predicted in a simple and efficient mode, and intelligent retention is achieved.

FIG. 3 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure.

As shown in FIG. 3, the method for losing user retention specifically includes operations S310-S350. Operations S310 and S340 to S350 may be implemented in the same manner as operations S210 and S230 to S240, respectively, and repeated parts will not be described in detail.

In operation S310, user information of a user is acquired, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

In operation S320, the users are classified according to the user information of the users by using an unsupervised clustering algorithm, so as to obtain a potentially lost user group.

Before inputting the user information acquired in step S310 into the neural network model, unsupervised clustering algorithm may be used to classify users according to their user information (e.g., the user basic attribute information and user profile information described above) to acquire a potentially attrition user population. By adopting the method, the user groups which are likely to lose can be screened out from the mass data for subsequent operation, for example, the neural network model is used for predicting the screened out potential lost user groups, so that the data volume input into the neural network model is reduced, and the data processing efficiency is improved.

In operation S330, user information of users in the potentially churning user group is input into the neural network model, and a first probability output by the neural network model and representing the churning degree of the users is obtained.

In this operation S330, the training of the neural network model may be performed by using the user information (e.g., the user basic attribute information and the user portrait information described above) of the users in the potential attrition user group screened in step S320, so as to improve the prediction accuracy of the neural network model. Specifically, the training process of the neural network model is similar to the above-described process, and is not repeated here.

And inputting user information of users in the potential user loss group into the trained neural network model to obtain a first probability which is output by the neural network model and used for representing the user loss degree.

In operation S340, a screening operation is performed on the users based on the first probability, and a list of potential attrition users is generated.

In operation S350, targeted financial product information is pushed to users in the list of potentially attrition users according to the user basic attribute information.

FIG. 4 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure.

As shown in FIG. 4, the method for losing user retention specifically includes operations S410-S460. Operations S410 to S420 and S450 may be implemented in the same manner as operations S210 to S220 and S240, respectively, and repeated parts will not be described in detail.

In operation S410, user information of a user is acquired, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

In operation S420, user information of a user is input into a neural network model, and a first probability output by the neural network model and used for representing a user churn degree is obtained.

In operations S430 to S440, comparing the first probability with a first preset threshold, and adding the user to the list of potential lost users when the first probability is not less than the first preset threshold; otherwise, operation S460 is performed.

In the embodiment of the present disclosure, the first preset threshold may be, for example, a preset value, or a preset range, and may be specifically set according to an actual situation, which is not limited herein.

When only the loss condition of the user within a certain threshold range needs to be concerned, a preset condition may be set that the first probability is not less than a first preset threshold, specifically, the first probability is compared with the first preset threshold, and when the first probability is not less than the first preset threshold, the user is added to the potential loss user list. By adopting the method, the potential lost users meeting the requirement can be screened out, the potential lost users are added into the potential lost user list, and the users are followed by paying attention to the potential lost users.

In operation S450, targeted financial product information is pushed to users in the list of potentially attrition users according to the user basic attribute information.

In operation S460, it ends.

In some embodiments, the users listed in the list of potential attrition users may be periodically pushed the information about the financial products they are interested in for retention purposes, as will be described in detail below with reference to fig. 5.

FIG. 5 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure.

As shown in fig. 5, the method for losing user retention specifically includes operations S510 to S540. Operations S510 to S530 may be implemented in the same manner as operations S210 to S230, and repeated details will not be repeated.

In operation S510, user information of a user is obtained, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

In operation S520, user information of a user is input into a neural network model, and a first probability output by the neural network model and used for representing a user churn degree is obtained.

In operation S530, a screening operation is performed on the users based on the first probability, and a list of potential attrition users is generated.

In operation S540, targeted financial product information is periodically transmitted to at least one user in the list of potentially attrition users.

For at least one user added to the list of potential attrition users, information about financial products that are of interest to those users, such as offer information about financial products, may be periodically transmitted. The periodic transmission may be set, for example, once a day, once a week, once a month, or at certain intervals, and may be specifically set according to the access history of the users, which is not limited herein.

By adopting the method, the users in the list of the potential lost users can be concerned periodically, so that the data volume of the potential lost users needing to be concerned is reduced, the users in the list of the potential lost users can be retained in time, and the retention effect of the potential lost users is improved.

It should be noted that, in operation S540, the step of periodically sending the targeted financial product information to at least one user in the list of potentially attrition users may be independent of operation S240, that is, the step of periodically sending the targeted financial product information to at least one user in the list of potentially attrition users may be performed directly according to the list of potentially attrition users without being premised on operation S240.

Although the various steps of the method are described above in a particular order, embodiments of the present disclosure are not so limited and the steps described above may be performed in other orders as desired. For example, in some embodiments, step S540 may be performed before step S450, or simultaneously with step S450. In some embodiments, operation S540 may also be performed simultaneously with operation S240, or after operation S240, which is not limited by the present disclosure.

According to the embodiment of the disclosure, by periodically pushing the interested financial product information of the users in the list of the potential lost users, the potential lost users needing important attention can be determined from the massive user data to be used for subsequent operations, for example, the users can be concerned and retained in a grading manner; and the information push can be avoided or the frequency of the push can be reduced for the users who are not in the potential attrition user list.

FIG. 6 schematically illustrates a flow chart of a method of churning user retention according to another embodiment of the present disclosure.

As shown in fig. 6, the method for losing user retention specifically includes operations S610 to S630. Operations S610 to S620 may be implemented in the same manner as operations S210 to S220, and repeated parts will not be described in detail.

In operation S610, user information of a user is acquired, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

In operation S620, user information of a user is input into a neural network model, and a first probability output by the neural network model and used for representing a user churn degree is obtained.

In operation S630, the first probability is compared with a second preset threshold, and in the case that the first probability is not less than the second preset threshold, the user is added to the list of the lost users.

In the embodiment of the present disclosure, the second preset threshold may be, for example, a preset value, or a preset range, and may be specifically set according to an actual situation, which is not limited herein. Wherein the second preset threshold is different from the first preset threshold.

When the first probability output by the neural network model exceeds a certain preset range or value, it can be determined that the user has a large possibility of losing, and the user can be added to the lost user list for such user, and then the user will not be concerned and kept.

In the embodiment of the present disclosure, after the first probability is obtained, the first probability is compared with a second preset threshold, and in a case that the first probability is not less than the second preset threshold, the user is added to the lost user list. For example, when the first probability is not less than 85% (i.e., the second preset threshold is 85%), or the first probability is not less than the range of 80% -90%, the users are added to the list of the lost users, and then the users will not be focused any more.

It should be noted that, in operation S630, the determination of the losed user may be independent of operation S240, that is, the determination of the losed user may be performed in operation S630 according to the first probability without taking operation S240 as a precondition.

Although the various steps of the method are described above in a particular order, embodiments of the present disclosure are not so limited and the steps described above may be performed in other orders as desired. For example, in some embodiments, step S630 may be performed before step S450, or simultaneously with step S450. In some embodiments, operation S630 may also be performed simultaneously with operation S240, or after operation S240, which is not limited by the present disclosure.

According to the loss user retention method, the user basic attribute information and the user portrait information are input into the neural network to predict user loss, so that the potential loss user can be accurately predicted in a simple and efficient mode, and intelligent retention is achieved. According to the embodiment of the disclosure, the financial product information interested by at least one user in the list of the potentially lost users is periodically sent to the user, so that while the data volume of the potentially lost users needing attention is reduced, the users in the list of the potentially lost users can be ensured to be retained in time, and the retention effect of the potentially lost users is improved.

Based on the method for losing user retention, the disclosure also provides a device for losing user retention. The apparatus will be described in detail below with reference to fig. 7 to 10.

Fig. 7 schematically illustrates a block diagram of an apparatus for churning user retention according to an embodiment of the present disclosure.

As shown in fig. 7, the apparatus 700 for churning user retention includes an acquisition module 710, a filtering module 720, a first adding module 730, and a pushing module 740.

The obtaining module 710 is configured to obtain user information of a user, where the user information includes user basic attribute information and user portrait information, and the user portrait information is used to represent an access situation of the user within a preset time period.

The screening module 720 is configured to input user information of the user into the neural network model, and obtain a first probability output by the neural network model and used for representing the user churn degree.

The first adding module 730 is configured to perform a filtering operation on the users based on the first probability, and generate a list of potential attrition users.

The pushing module 740 is configured to push targeted financial product information to users in the list of potentially attrition users according to the user basic attribute information.

In some embodiments of the present disclosure, the first adding module 730 is further configured to compare the first probability with a first preset threshold, and add the user to the list of potential attrition users if the first probability is not less than the first preset threshold.

In some embodiments of the present disclosure, the user profile information includes, but is not limited to, a number of times the user logs into the mobile client, a number of times the user conducts transactions, a length of time the user accesses the mobile client, and a number of times the user accesses content related to the financial product, wherein the content related to the financial product includes at least one of a product description of the financial product, offer information of the financial product, and other products associated with the financial product.

In some embodiments of the present disclosure, the user basic attribute information includes at least one of user age, user gender, educational background, professional background, asset information.

In some embodiments of the present disclosure, the financial product information includes at least one of offer information, financial product introduction, and status information of the financial product.

Fig. 8 schematically illustrates a block diagram of an apparatus for churning user retention according to another embodiment of the present disclosure.

As shown in fig. 8, the apparatus 800 for attrition user retention includes an obtaining module 810, a classifying module 820, a filtering module 830, a first adding module 840, and a pushing module 850. The obtaining module 810, the screening module 830, the first adding module 840, and the pushing module 850 respectively have functions similar to or the same as those of the obtaining module 710, the screening module 720, the first adding module 730, and the pushing module 740, and repeated descriptions thereof are omitted.

In the embodiment of the present disclosure, the classification module 820 is configured to classify users according to user information of the users by using an unsupervised clustering algorithm, so as to obtain a potentially-lost user group.

The screening module 830 is further configured to input user information of users in the potentially churning user group into the neural network model, and obtain a first probability output by the neural network model and used for representing the churning degree of the users.

Fig. 9 schematically illustrates a block diagram of an apparatus for churning user retention according to another embodiment of the present disclosure.

As shown in fig. 9, the apparatus 900 for churning user retention includes an acquisition module 910, a filtering module 920, a first adding module 930, a pushing module 940, and an updating module 950. The obtaining module 910, the screening module 920, the first adding module 930, and the pushing module 940 respectively have the same functions as the obtaining module 710, the screening module 720, the first adding module 730, and the pushing module 740, and repeated parts are not described again.

In the disclosed embodiment, the update module 950 is configured to periodically send targeted financial product information to at least one user in the list of potentially attrition users.

Fig. 10 schematically illustrates a block diagram of an apparatus for churning user retention according to another embodiment of the present disclosure.

As shown in fig. 10, the apparatus 1000 for churning user retention includes an acquisition module 1010, a filtering module 1020, a first adding module 1030, a pushing module 1040, and a second adding module 1050. The obtaining module 1010, the screening module 1020, the first adding module 1030, and the pushing module 1040 respectively have the same functions as the obtaining module 710, the screening module 720, the first adding module 730, and the pushing module 740, and repeated parts are not described again.

In the embodiment of the present disclosure, the second adding module 1050 is configured to compare the first probability with a second preset threshold, and add the user to the lost user list if the first probability is not less than the second preset threshold.

It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module/unit/subunit and the like in the apparatus part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described herein again.

According to the embodiment of the present disclosure, any plurality of the obtaining module 710, the screening module 720, the first adding module 730, and the pushing module 740 may be combined into one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 710, the screening module 720, the first adding module 730, and the pushing module 740 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or an appropriate combination of any several of them. Alternatively, at least one of the obtaining module 710, the filtering module 720, the first adding module 730, and the pushing module 740 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and do not violate the good custom of the public order.

Fig. 11 schematically illustrates a block diagram of an electronic device adapted to implement a method of churning user retention according to an embodiment of the present disclosure.

As shown in fig. 11, an electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1102 or a program loaded from a storage section 1108 into a Random Access Memory (RAM) 1103. The processor 1101 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 1101 may also include on-board memory for caching purposes. The processor 1101 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to the embodiments of the present disclosure.

In the RAM 1103, various programs and data necessary for the operation of the electronic device 1100 are stored. The processor 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. The processor 1101 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1102 and/or the RAM 1103. It is noted that the programs may also be stored in one or more memories other than the ROM 1102 and RAM 1103. The processor 1101 may also perform various operations of the method flows according to the embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 1100 may also include input/output (I/O) interface 1105, input/output (I/O) interface 1105 also connected to bus 1104, according to an embodiment of the disclosure. Electronic device 1100 may also include one or more of the following components connected to I/O interface 1105: an input portion 1106 including a keyboard, mouse, and the like; an output portion 1107 including a signal output unit such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1108 including a hard disk and the like; and a communication section 1109 including a network interface card such as a LAN card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. A driver 1110 is also connected to the I/O interface 1105 as necessary. A removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1110 as necessary, so that a computer program read out therefrom is mounted into the storage section 1108 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 1102 and/or the RAM 1103 and/or one or more memories other than the ROM 1102 and the RAM 1103 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the item recommendation method provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 1101. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication part 1109, and/or installed from the removable medium 1111. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 1109 and/or installed from the removable medium 1111. The computer program, when executed by the processor 1101, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A method of churning user retention, comprising:

2. The method of churning user retention according to claim 1, further comprising:

3. The attrition user retention method of claim 1 wherein the performing a filtering operation on the user based on the first probability to generate a list of potential attrition users comprises:

4. The method of churning user retention according to claim 1, further comprising:

periodically sending targeted financial product information to at least one user in the list of potentially attrition users.

5. The method of churning user retention according to claim 1, further comprising:

and comparing the first probability with a second preset threshold, and adding the user to a lost user list under the condition that the first probability is not less than the second preset threshold.

6. The attrition user retention method of any one of claims 1 to 5 wherein the user profile information comprises at least one of:

the number of times that the user logs in the mobile client;

the number of times the user makes transactions;

the duration of time that the user accesses the mobile client; and

7. The attrition user retention method of any one of claims 1-5 wherein the user base attribute information comprises at least one of user age, user gender, educational background, occupational background, asset information.

8. The method of attrition user retention according to any one of claims 1 to 5 wherein the financial product information includes at least one of offer information, financial product introduction and financial product status information.

9. An apparatus to lose user retention, comprising:

10. The apparatus for churning user retention according to claim 9, further comprising:

11. The apparatus of attrition user retention of claim 9 wherein the first adding module is further configured to:

12. The apparatus for churning user retention according to claim 9, further comprising:

13. The apparatus for churning user retention according to claim 9, further comprising:

14. An attrition user retained device as claimed in any one of claims 9 to 13 wherein the user profile information comprises at least one of:

the number of times that the user logs in the mobile client;

the number of times the user makes transactions;

the duration of time that the user accesses the mobile client; and

15. The attrition user-retained apparatus of any one of claims 9 to 13 wherein the user base attribute information comprises at least one of user age, user gender, educational background, occupational background, asset information.

16. The attrition user-retained apparatus of any one of claims 9 to 13 wherein the financial product information comprises at least one of offer information, financial product introduction and financial product status information.

17. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.

18. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 8.

19. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 8.